'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase					=logging.get_logger(__name__)






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       YolosConfig:
    __lowerCamelCase            =    YolosConfig()

    # size of the architecture
    if "yolos_ti" in yolos_name:
        __lowerCamelCase            =    1_92
        __lowerCamelCase            =    7_68
        __lowerCamelCase            =    12
        __lowerCamelCase            =    3
        __lowerCamelCase            =    [8_00, 13_33]
        __lowerCamelCase            =    False
    elif yolos_name == "yolos_s_dWr":
        __lowerCamelCase            =    3_30
        __lowerCamelCase            =    14
        __lowerCamelCase            =    6
        __lowerCamelCase            =    13_20
    elif "yolos_s" in yolos_name:
        __lowerCamelCase            =    3_84
        __lowerCamelCase            =    15_36
        __lowerCamelCase            =    12
        __lowerCamelCase            =    6
    elif "yolos_b" in yolos_name:
        __lowerCamelCase            =    [8_00, 13_44]

    __lowerCamelCase            =    91
    __lowerCamelCase            =    '''huggingface/label-files'''
    __lowerCamelCase            =    '''coco-detection-id2label.json'''
    __lowerCamelCase            =    json.load(open(hf_hub_download(UpperCamelCase__							,    UpperCamelCase__							,    repo_type='''dataset'''							)							,    '''r'''							)							)
    __lowerCamelCase            =    {int(UpperCamelCase__							): v for k, v in idalabel.items()}
    __lowerCamelCase            =    idalabel
    __lowerCamelCase            =    {v: k for k, v in idalabel.items()}

    return config






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__ = False							)      ->       Tuple:
    for i in range(config.num_hidden_layers							):
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        __lowerCamelCase            =    state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""							)
        __lowerCamelCase            =    state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""							)
        # next, add query, keys and values (in that order) to the state dict
        __lowerCamelCase            =    in_proj_weight[: config.hidden_size, :]
        __lowerCamelCase            =    in_proj_bias[: config.hidden_size]
        __lowerCamelCase            =    in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        __lowerCamelCase            =    in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        __lowerCamelCase            =    in_proj_weight[-config.hidden_size :, :]
        __lowerCamelCase            =    in_proj_bias[-config.hidden_size :]






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       str:
    if "backbone" in name:
        __lowerCamelCase            =    name.replace('''backbone'''							,    '''vit'''							)
    if "cls_token" in name:
        __lowerCamelCase            =    name.replace('''cls_token'''							,    '''embeddings.cls_token'''							)
    if "det_token" in name:
        __lowerCamelCase            =    name.replace('''det_token'''							,    '''embeddings.detection_tokens'''							)
    if "mid_pos_embed" in name:
        __lowerCamelCase            =    name.replace('''mid_pos_embed'''							,    '''encoder.mid_position_embeddings'''							)
    if "pos_embed" in name:
        __lowerCamelCase            =    name.replace('''pos_embed'''							,    '''embeddings.position_embeddings'''							)
    if "patch_embed.proj" in name:
        __lowerCamelCase            =    name.replace('''patch_embed.proj'''							,    '''embeddings.patch_embeddings.projection'''							)
    if "blocks" in name:
        __lowerCamelCase            =    name.replace('''blocks'''							,    '''encoder.layer'''							)
    if "attn.proj" in name:
        __lowerCamelCase            =    name.replace('''attn.proj'''							,    '''attention.output.dense'''							)
    if "attn" in name:
        __lowerCamelCase            =    name.replace('''attn'''							,    '''attention.self'''							)
    if "norm1" in name:
        __lowerCamelCase            =    name.replace('''norm1'''							,    '''layernorm_before'''							)
    if "norm2" in name:
        __lowerCamelCase            =    name.replace('''norm2'''							,    '''layernorm_after'''							)
    if "mlp.fc1" in name:
        __lowerCamelCase            =    name.replace('''mlp.fc1'''							,    '''intermediate.dense'''							)
    if "mlp.fc2" in name:
        __lowerCamelCase            =    name.replace('''mlp.fc2'''							,    '''output.dense'''							)
    if "class_embed" in name:
        __lowerCamelCase            =    name.replace('''class_embed'''							,    '''class_labels_classifier'''							)
    if "bbox_embed" in name:
        __lowerCamelCase            =    name.replace('''bbox_embed'''							,    '''bbox_predictor'''							)
    if "vit.norm" in name:
        __lowerCamelCase            =    name.replace('''vit.norm'''							,    '''vit.layernorm'''							)

    return name






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       dict:
    for key in orig_state_dict.copy().keys():
        __lowerCamelCase            =    orig_state_dict.pop(UpperCamelCase__							)

        if "qkv" in key:
            __lowerCamelCase            =    key.split('''.'''							)
            __lowerCamelCase            =    int(key_split[2]							)
            __lowerCamelCase            =    model.vit.encoder.layer[layer_num].attention.attention.all_head_size
            if "weight" in key:
                __lowerCamelCase            =    val[:dim, :]
                __lowerCamelCase            =    val[
                    dim : dim * 2, :
                ]
                __lowerCamelCase            =    val[-dim:, :]
            else:
                __lowerCamelCase            =    val[:dim]
                __lowerCamelCase            =    val[dim : dim * 2]
                __lowerCamelCase            =    val[-dim:]
        else:
            __lowerCamelCase            =    val

    return orig_state_dict






def __lowerCAmelCase					(				)      ->       torch.Tensor:
    __lowerCamelCase            =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __lowerCamelCase            =    Image.open(requests.get(UpperCamelCase__							,    stream=UpperCamelCase__							).raw							)
    return im






@torch.no_grad()
def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__ = False							)      ->       List[str]:
    __lowerCamelCase            =    get_yolos_config(UpperCamelCase__							)

    # load original state_dict
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location='''cpu'''							)['''model''']

    # load 🤗 model
    __lowerCamelCase            =    YolosForObjectDetection(UpperCamelCase__							)
    model.eval()
    __lowerCamelCase            =    convert_state_dict(UpperCamelCase__							,    UpperCamelCase__							)
    model.load_state_dict(UpperCamelCase__							)

    # Check outputs on an image, prepared by YolosImageProcessor
    __lowerCamelCase            =    8_00 if yolos_name != '''yolos_ti''' else 5_12
    __lowerCamelCase            =    YolosImageProcessor(format='''coco_detection'''							,    size=UpperCamelCase__							)
    __lowerCamelCase            =    image_processor(images=prepare_img()							,    return_tensors='''pt'''							)
    __lowerCamelCase            =    model(**UpperCamelCase__							)
    __lowerCamelCase      ,    __lowerCamelCase            =    outputs.logits, outputs.pred_boxes

    __lowerCamelCase      ,    __lowerCamelCase            =    None, None
    if yolos_name == "yolos_ti":
        __lowerCamelCase            =    torch.tensor(
            [[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]]							)
        __lowerCamelCase            =    torch.tensor(
            [[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]]							)
    elif yolos_name == "yolos_s_200_pre":
        __lowerCamelCase            =    torch.tensor(
            [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]							)
        __lowerCamelCase            =    torch.tensor(
            [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]							)
    elif yolos_name == "yolos_s_300_pre":
        __lowerCamelCase            =    torch.tensor(
            [[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]]							)
        __lowerCamelCase            =    torch.tensor(
            [[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]]							)
    elif yolos_name == "yolos_s_dWr":
        __lowerCamelCase            =    torch.tensor(
            [[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]]							)
        __lowerCamelCase            =    torch.tensor(
            [[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]]							)
    elif yolos_name == "yolos_base":
        __lowerCamelCase            =    torch.tensor(
            [[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]]							)
        __lowerCamelCase            =    torch.tensor(
            [[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]]							)
    else:
        raise ValueError(f"""Unknown yolos_name: {yolos_name}"""							)

    assert torch.allclose(logits[0, :3, :3]							,    UpperCamelCase__							,    atol=1E-4							)
    assert torch.allclose(pred_boxes[0, :3, :3]							,    UpperCamelCase__							,    atol=1E-4							)

    Path(UpperCamelCase__							).mkdir(exist_ok=UpperCamelCase__							)
    print(f"""Saving model {yolos_name} to {pytorch_dump_folder_path}"""							)
    model.save_pretrained(UpperCamelCase__							)
    print(f"""Saving image processor to {pytorch_dump_folder_path}"""							)
    image_processor.save_pretrained(UpperCamelCase__							)

    if push_to_hub:
        __lowerCamelCase            =    {
            '''yolos_ti''': '''yolos-tiny''',
            '''yolos_s_200_pre''': '''yolos-small''',
            '''yolos_s_300_pre''': '''yolos-small-300''',
            '''yolos_s_dWr''': '''yolos-small-dwr''',
            '''yolos_base''': '''yolos-base''',
        }

        print('''Pushing to the hub...'''							)
        __lowerCamelCase            =    model_mapping[yolos_name]
        image_processor.push_to_hub(UpperCamelCase__							,    organization='''hustvl'''							)
        model.push_to_hub(UpperCamelCase__							,    organization='''hustvl'''							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--yolos_name",
      default="yolos_s_200_pre",
      type=str,
      help=(
          "Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"
          " 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."
      ),
  )
  parser.add_argument(
      "--checkpoint_path", default=None, type=str, help="Path to the original state dict (.pth file)."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )

  __UpperCAmelCase					=parser.parse_args()
  convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)

'''simple docstring'''




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase					=logging.get_logger(__name__)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''projector.weight''']
    __lowerCamelCase            =    downstream_dict['''projector.bias''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''model.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       List[Any]:
    __lowerCamelCase            =    UniSpeechSatForXVector.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''connector.weight''']
    __lowerCamelCase            =    downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
        __lowerCamelCase            =    downstream_dict[
            f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
        ]
        __lowerCamelCase            =    downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    __lowerCamelCase            =    downstream_dict['''objective.W''']
    return model






@torch.no_grad()
def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location='''cpu'''							)

    __lowerCamelCase            =    checkpoint['''Downstream''']

    __lowerCamelCase            =    UniSpeechSatConfig.from_pretrained(UpperCamelCase__							)
    __lowerCamelCase            =    WavaVecaFeatureExtractor.from_pretrained(
        UpperCamelCase__							,    return_attention_mask=UpperCamelCase__							,    do_normalize=UpperCamelCase__							)

    __lowerCamelCase            =    hf_config.architectures[0]
    if arch.endswith('''ForSequenceClassification'''							):
        __lowerCamelCase            =    convert_classification(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForAudioFrameClassification'''							):
        __lowerCamelCase            =    convert_diarization(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForXVector'''							):
        __lowerCamelCase            =    convert_xvector(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    else:
        raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""							)

    if hf_config.use_weighted_layer_sum:
        __lowerCamelCase            =    checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(UpperCamelCase__							)
    hf_model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  parser.add_argument(
      "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  )
  parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  __UpperCAmelCase					=parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
